Papers with GM-LVeGs to part-of-speech tagging
Gaussian Mixture Latent Vector Grammars (P18-1)
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| Challenge: | Existing models of latent variable grammars are not observable in treebanks, so latent variables are learned using expectation-maximization. |
| Approach: | They propose a new framework that extends latent variable grammars such that each nonterminal symbol is associated with a continuous vector space representing the set of (infinitely many) subtypes of the nonterminals. |
| Outcome: | The proposed framework can achieve competitive accuracies in part-of-speech tagging and constituency parsing. |